Overview

Dataset statistics

Number of variables12
Number of observations13393
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory1.2 MiB
Average record size in memory96.0 B

Variable types

Numeric10
Categorical2

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
height_cm is highly overall correlated with weight_kg and 4 other fieldsHigh correlation
weight_kg is highly overall correlated with height_cm and 3 other fieldsHigh correlation
body fat_% is highly overall correlated with height_cm and 4 other fieldsHigh correlation
diastolic is highly overall correlated with systolicHigh correlation
systolic is highly overall correlated with diastolicHigh correlation
gripForce is highly overall correlated with height_cm and 5 other fieldsHigh correlation
sit-ups counts is highly overall correlated with body fat_% and 2 other fieldsHigh correlation
broad jump_cm is highly overall correlated with height_cm and 5 other fieldsHigh correlation
gender is highly overall correlated with height_cm and 4 other fieldsHigh correlation
class is uniformly distributedUniform

Reproduction

Analysis started2023-06-24 12:14:33.851732
Analysis finished2023-06-24 12:14:45.075996
Duration11.22 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct44
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.775106
Minimum21
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.8 KiB
2023-06-24T14:14:45.158963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q125
median32
Q348
95-th percentile62
Maximum64
Range43
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.625639
Coefficient of variation (CV)0.37051258
Kurtosis-1.0176715
Mean36.775106
Median Absolute Deviation (MAD)9
Skewness0.59989554
Sum492529
Variance185.65805
MonotonicityNot monotonic
2023-06-24T14:14:45.269755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
21 964
 
7.2%
22 789
 
5.9%
23 668
 
5.0%
25 644
 
4.8%
26 629
 
4.7%
24 617
 
4.6%
27 546
 
4.1%
28 527
 
3.9%
29 407
 
3.0%
30 374
 
2.8%
Other values (34) 7228
54.0%
ValueCountFrequency (%)
21 964
7.2%
22 789
5.9%
23 668
5.0%
24 617
4.6%
25 644
4.8%
26 629
4.7%
27 546
4.1%
28 527
3.9%
29 407
3.0%
30 374
 
2.8%
ValueCountFrequency (%)
64 215
1.6%
63 230
1.7%
62 265
2.0%
61 254
1.9%
60 368
2.7%
59 192
1.4%
58 180
1.3%
57 181
1.4%
56 197
1.5%
55 185
1.4%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.8 KiB
M
8467 
F
4926 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 8467
63.2%
F 4926
36.8%

Length

2023-06-24T14:14:45.354100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-24T14:14:45.428186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
m 8467
63.2%
f 4926
36.8%

Most occurring characters

ValueCountFrequency (%)
M 8467
63.2%
F 4926
36.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 13393
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 8467
63.2%
F 4926
36.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 13393
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 8467
63.2%
F 4926
36.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 8467
63.2%
F 4926
36.8%

height_cm
Real number (ℝ)

Distinct467
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168.55981
Minimum125
Maximum193.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.8 KiB
2023-06-24T14:14:45.504054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum125
5-th percentile154.2
Q1162.4
median169.2
Q3174.8
95-th percentile181.5
Maximum193.8
Range68.8
Interquartile range (IQR)12.4

Descriptive statistics

Standard deviation8.4265826
Coefficient of variation (CV)0.049991648
Kurtosis-0.4330535
Mean168.55981
Median Absolute Deviation (MAD)6.1
Skewness-0.18688235
Sum2257521.5
Variance71.007293
MonotonicityNot monotonic
2023-06-24T14:14:45.594667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
170 126
 
0.9%
173 112
 
0.8%
175 103
 
0.8%
171 101
 
0.8%
172 94
 
0.7%
167 93
 
0.7%
172.5 81
 
0.6%
174 81
 
0.6%
168 79
 
0.6%
164 78
 
0.6%
Other values (457) 12445
92.9%
ValueCountFrequency (%)
125 1
< 0.1%
139.5 1
< 0.1%
139.8 1
< 0.1%
139.9 1
< 0.1%
140.5 1
< 0.1%
141 1
< 0.1%
143.4 1
< 0.1%
143.6 1
< 0.1%
143.7 1
< 0.1%
143.8 1
< 0.1%
ValueCountFrequency (%)
193.8 1
 
< 0.1%
192 1
 
< 0.1%
191.9 1
 
< 0.1%
191.8 3
< 0.1%
191.7 1
 
< 0.1%
191.6 1
 
< 0.1%
191.4 2
< 0.1%
191.3 2
< 0.1%
190.9 2
< 0.1%
190.8 1
 
< 0.1%

weight_kg
Real number (ℝ)

Distinct1398
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.447316
Minimum26.3
Maximum138.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.8 KiB
2023-06-24T14:14:45.696376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum26.3
5-th percentile49.2
Q158.2
median67.4
Q375.3
95-th percentile87.3
Maximum138.1
Range111.8
Interquartile range (IQR)17.1

Descriptive statistics

Standard deviation11.949666
Coefficient of variation (CV)0.17717038
Kurtosis0.17160606
Mean67.447316
Median Absolute Deviation (MAD)8.54
Skewness0.34980459
Sum903321.9
Variance142.79453
MonotonicityNot monotonic
2023-06-24T14:14:45.790446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70.5 53
 
0.4%
70 51
 
0.4%
71 51
 
0.4%
73.2 49
 
0.4%
66 49
 
0.4%
60.9 49
 
0.4%
72.3 49
 
0.4%
71.8 48
 
0.4%
68.1 47
 
0.4%
74.1 47
 
0.4%
Other values (1388) 12900
96.3%
ValueCountFrequency (%)
26.3 1
 
< 0.1%
31.9 1
 
< 0.1%
33.7 1
 
< 0.1%
34.4 1
 
< 0.1%
34.5 1
 
< 0.1%
35.9 1
 
< 0.1%
36.5 1
 
< 0.1%
37.3 1
 
< 0.1%
37.4 2
< 0.1%
38.1 4
< 0.1%
ValueCountFrequency (%)
138.1 1
< 0.1%
135.78 1
< 0.1%
125.7 1
< 0.1%
123 1
< 0.1%
119.8 1
< 0.1%
119.6 1
< 0.1%
118.8 1
< 0.1%
118.6 1
< 0.1%
117.5 1
< 0.1%
117.4 1
< 0.1%

body fat_%
Real number (ℝ)

Distinct527
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.240165
Minimum3
Maximum78.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.8 KiB
2023-06-24T14:14:45.904357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile12.1
Q118
median22.8
Q328
95-th percentile35.74
Maximum78.4
Range75.4
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.2568441
Coefficient of variation (CV)0.31225441
Kurtosis0.12871219
Mean23.240165
Median Absolute Deviation (MAD)5
Skewness0.36113225
Sum311255.53
Variance52.661786
MonotonicityNot monotonic
2023-06-24T14:14:46.002161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.1 90
 
0.7%
24.5 87
 
0.6%
20.2 87
 
0.6%
22.8 87
 
0.6%
20.3 85
 
0.6%
25.9 83
 
0.6%
20.5 82
 
0.6%
24.7 80
 
0.6%
18.2 80
 
0.6%
21.2 79
 
0.6%
Other values (517) 12553
93.7%
ValueCountFrequency (%)
3 2
< 0.1%
3.5 3
< 0.1%
4 1
 
< 0.1%
4.5 1
 
< 0.1%
4.7 1
 
< 0.1%
4.9 3
< 0.1%
5 1
 
< 0.1%
5.5 3
< 0.1%
5.6 1
 
< 0.1%
5.8 2
< 0.1%
ValueCountFrequency (%)
78.4 1
< 0.1%
54.9 1
< 0.1%
53.5 1
< 0.1%
50.6 1
< 0.1%
50.3 1
< 0.1%
50.2 1
< 0.1%
49.8 1
< 0.1%
49.3 1
< 0.1%
49.2 1
< 0.1%
48.9 1
< 0.1%

diastolic
Real number (ℝ)

Distinct89
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.796842
Minimum0
Maximum156.2
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size104.8 KiB
2023-06-24T14:14:46.124529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile61
Q171
median79
Q386
95-th percentile96
Maximum156.2
Range156.2
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.742033
Coefficient of variation (CV)0.13632568
Kurtosis0.36352454
Mean78.796842
Median Absolute Deviation (MAD)8
Skewness-0.15963717
Sum1055326.1
Variance115.39128
MonotonicityNot monotonic
2023-06-24T14:14:46.236330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 670
 
5.0%
77 482
 
3.6%
75 475
 
3.5%
78 474
 
3.5%
81 464
 
3.5%
79 460
 
3.4%
83 448
 
3.3%
82 445
 
3.3%
76 428
 
3.2%
74 420
 
3.1%
Other values (79) 8627
64.4%
ValueCountFrequency (%)
0 1
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
30 1
 
< 0.1%
37 1
 
< 0.1%
40 1
 
< 0.1%
41 2
 
< 0.1%
42 6
< 0.1%
43 2
 
< 0.1%
44 3
< 0.1%
ValueCountFrequency (%)
156.2 1
 
< 0.1%
126 1
 
< 0.1%
121 1
 
< 0.1%
120 1
 
< 0.1%
118 1
 
< 0.1%
117 1
 
< 0.1%
115 1
 
< 0.1%
113 1
 
< 0.1%
112 3
< 0.1%
111 1
 
< 0.1%

systolic
Real number (ℝ)

Distinct102
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.23482
Minimum0
Maximum201
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size104.8 KiB
2023-06-24T14:14:46.344654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile106
Q1120
median130
Q3141
95-th percentile155
Maximum201
Range201
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.713954
Coefficient of variation (CV)0.11298018
Kurtosis0.38028484
Mean130.23482
Median Absolute Deviation (MAD)10
Skewness-0.04865361
Sum1744234.9
Variance216.50043
MonotonicityNot monotonic
2023-06-24T14:14:46.443788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 515
 
3.8%
130 416
 
3.1%
123 404
 
3.0%
134 335
 
2.5%
128 320
 
2.4%
118 319
 
2.4%
132 314
 
2.3%
125 314
 
2.3%
129 310
 
2.3%
122 310
 
2.3%
Other values (92) 9836
73.4%
ValueCountFrequency (%)
0 1
 
< 0.1%
14 1
 
< 0.1%
43.9 1
 
< 0.1%
77 1
 
< 0.1%
82 1
 
< 0.1%
84 1
 
< 0.1%
86 3
< 0.1%
88 2
< 0.1%
89 2
< 0.1%
90 2
< 0.1%
ValueCountFrequency (%)
201 1
 
< 0.1%
195 1
 
< 0.1%
193 2
< 0.1%
191 1
 
< 0.1%
188 1
 
< 0.1%
187 1
 
< 0.1%
186 1
 
< 0.1%
184 1
 
< 0.1%
181 3
< 0.1%
180 1
 
< 0.1%

gripForce
Real number (ℝ)

Distinct550
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.963877
Minimum0
Maximum70.5
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size104.8 KiB
2023-06-24T14:14:46.547262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20.9
Q127.5
median37.9
Q345.2
95-th percentile53.5
Maximum70.5
Range70.5
Interquartile range (IQR)17.7

Descriptive statistics

Standard deviation10.624864
Coefficient of variation (CV)0.28743911
Kurtosis-0.82220017
Mean36.963877
Median Absolute Deviation (MAD)8.7
Skewness0.018456493
Sum495057.21
Variance112.88774
MonotonicityNot monotonic
2023-06-24T14:14:46.646533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.1 71
 
0.5%
43.9 67
 
0.5%
40.6 59
 
0.4%
40.3 58
 
0.4%
39.3 58
 
0.4%
40.1 58
 
0.4%
25.4 58
 
0.4%
43.5 57
 
0.4%
44.5 57
 
0.4%
27.5 57
 
0.4%
Other values (540) 12793
95.5%
ValueCountFrequency (%)
0 3
< 0.1%
1.6 1
 
< 0.1%
2.1 1
 
< 0.1%
3.5 1
 
< 0.1%
4.4 1
 
< 0.1%
5.3 1
 
< 0.1%
6.7 1
 
< 0.1%
7.9 1
 
< 0.1%
8.6 1
 
< 0.1%
9.1 1
 
< 0.1%
ValueCountFrequency (%)
70.5 1
< 0.1%
70.4 1
< 0.1%
69.9 1
< 0.1%
69 1
< 0.1%
68.4 1
< 0.1%
67.6 1
< 0.1%
67.1 2
< 0.1%
66.8 1
< 0.1%
66 1
< 0.1%
65.8 1
< 0.1%

sit and bend forward_cm
Real number (ℝ)

Distinct528
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.209268
Minimum-25
Maximum213
Zeros12
Zeros (%)0.1%
Negative642
Negative (%)4.8%
Memory size104.8 KiB
2023-06-24T14:14:46.858317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-25
5-th percentile0.2
Q110.9
median16.2
Q320.7
95-th percentile26.54
Maximum213
Range238
Interquartile range (IQR)9.8

Descriptive statistics

Standard deviation8.456677
Coefficient of variation (CV)0.55602129
Kurtosis35.220856
Mean15.209268
Median Absolute Deviation (MAD)4.9
Skewness0.78549201
Sum203697.73
Variance71.515386
MonotonicityNot monotonic
2023-06-24T14:14:46.955015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 119
 
0.9%
18.5 102
 
0.8%
16 102
 
0.8%
19 100
 
0.7%
17 99
 
0.7%
15 98
 
0.7%
21 95
 
0.7%
14 92
 
0.7%
22 89
 
0.7%
18 87
 
0.6%
Other values (518) 12410
92.7%
ValueCountFrequency (%)
-25 1
 
< 0.1%
-22 1
 
< 0.1%
-20 10
0.1%
-19.9 2
 
< 0.1%
-19.7 1
 
< 0.1%
-19 2
 
< 0.1%
-18.9 1
 
< 0.1%
-18.7 1
 
< 0.1%
-18.4 1
 
< 0.1%
-18 1
 
< 0.1%
ValueCountFrequency (%)
213 1
 
< 0.1%
185 1
 
< 0.1%
42 1
 
< 0.1%
40 2
 
< 0.1%
37 1
 
< 0.1%
35.2 5
< 0.1%
35 1
 
< 0.1%
34.8 2
 
< 0.1%
34.7 1
 
< 0.1%
34.6 2
 
< 0.1%

sit-ups counts
Real number (ℝ)

Distinct81
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.771224
Minimum0
Maximum80
Zeros125
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size104.8 KiB
2023-06-24T14:14:47.053427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q130
median41
Q350
95-th percentile60
Maximum80
Range80
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.276698
Coefficient of variation (CV)0.35897056
Kurtosis-0.15632586
Mean39.771224
Median Absolute Deviation (MAD)10
Skewness-0.46782988
Sum532656
Variance203.82412
MonotonicityNot monotonic
2023-06-24T14:14:47.153124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 416
 
3.1%
40 394
 
2.9%
50 393
 
2.9%
46 383
 
2.9%
47 369
 
2.8%
48 368
 
2.7%
44 367
 
2.7%
43 365
 
2.7%
51 362
 
2.7%
42 347
 
2.6%
Other values (71) 9629
71.9%
ValueCountFrequency (%)
0 125
0.9%
1 18
 
0.1%
2 31
 
0.2%
3 23
 
0.2%
4 28
 
0.2%
4.6 1
 
< 0.1%
5 29
 
0.2%
6 28
 
0.2%
7 36
 
0.3%
8 34
 
0.3%
ValueCountFrequency (%)
80 1
 
< 0.1%
78 2
 
< 0.1%
76 4
 
< 0.1%
75 4
 
< 0.1%
74 4
 
< 0.1%
73 2
 
< 0.1%
72 3
 
< 0.1%
71 15
0.1%
70 11
0.1%
69 19
0.1%

broad jump_cm
Real number (ℝ)

Distinct245
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190.12963
Minimum0
Maximum303
Zeros10
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size104.8 KiB
2023-06-24T14:14:47.253050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile121
Q1162
median193
Q3221
95-th percentile247
Maximum303
Range303
Interquartile range (IQR)59

Descriptive statistics

Standard deviation39.868
Coefficient of variation (CV)0.20968852
Kurtosis0.0023965018
Mean190.12963
Median Absolute Deviation (MAD)29
Skewness-0.42262256
Sum2546406.1
Variance1589.4574
MonotonicityNot monotonic
2023-06-24T14:14:47.351569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
211 181
 
1.4%
220 176
 
1.3%
230 172
 
1.3%
180 161
 
1.2%
200 157
 
1.2%
215 144
 
1.1%
170 140
 
1.0%
185 140
 
1.0%
226 139
 
1.0%
222 139
 
1.0%
Other values (235) 11844
88.4%
ValueCountFrequency (%)
0 10
0.1%
20 1
 
< 0.1%
35 1
 
< 0.1%
40 1
 
< 0.1%
43 1
 
< 0.1%
45 1
 
< 0.1%
47 1
 
< 0.1%
49 1
 
< 0.1%
50 2
 
< 0.1%
51 2
 
< 0.1%
ValueCountFrequency (%)
303 1
 
< 0.1%
299 1
 
< 0.1%
295 2
< 0.1%
294 1
 
< 0.1%
293 1
 
< 0.1%
290 2
< 0.1%
288 1
 
< 0.1%
286 2
< 0.1%
285 3
< 0.1%
284 1
 
< 0.1%

class
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.8 KiB
C
3349 
D
3349 
A
3348 
B
3347 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13393
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowA
3rd rowC
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
C 3349
25.0%
D 3349
25.0%
A 3348
25.0%
B 3347
25.0%

Length

2023-06-24T14:14:47.439124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-24T14:14:47.527598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
c 3349
25.0%
d 3349
25.0%
a 3348
25.0%
b 3347
25.0%

Most occurring characters

ValueCountFrequency (%)
C 3349
25.0%
D 3349
25.0%
A 3348
25.0%
B 3347
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 13393
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 3349
25.0%
D 3349
25.0%
A 3348
25.0%
B 3347
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13393
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 3349
25.0%
D 3349
25.0%
A 3348
25.0%
B 3347
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 3349
25.0%
D 3349
25.0%
A 3348
25.0%
B 3347
25.0%

Interactions

2023-06-24T14:14:43.693577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:34.429075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:35.458123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:36.411585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:37.493417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:38.481767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:39.514854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:40.637785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:41.616004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:42.679394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:43.785725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:34.623561image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:35.544859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:36.497958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:37.593547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:38.574066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:39.613182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:40.724971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:41.704428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:42.773422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:43.878804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:34.713274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:35.641080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:36.590057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:37.695499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:38.676613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:39.826870image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:40.822382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:41.795775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:42.871691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:43.970557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:34.797832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:35.727358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:36.683272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:37.784788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:38.773317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:39.931820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:40.913229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:41.893451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:42.963012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:44.065804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:34.884514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:35.828004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:36.779920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:37.891504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:38.871512image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:40.039612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:41.020351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:41.995288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:43.062294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:44.171129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:34.991192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:35.926850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:36.882080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:37.994489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:38.973295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:40.137992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:41.116457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:42.090058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:43.173694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:44.316022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:35.093399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:36.025674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:36.979227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:38.096685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:39.071636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:40.233750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:41.218003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:42.191820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:43.290768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:44.426227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:35.182282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:36.121893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:37.197133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:38.196206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:39.166531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:40.341020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:41.314838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:42.404001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:43.391978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:44.515323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:35.272218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:36.213408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:37.283400image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:38.290572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:39.264747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:40.438263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:41.406129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:42.491191image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:43.487583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:44.613659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:35.365460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:36.323077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:37.388643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:38.391663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:39.410027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:40.539639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:41.511701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:42.588111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-24T14:14:43.597203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-06-24T14:14:47.608702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ageheight_cmweight_kgbody fat_%diastolicsystolicgripForcesit and bend forward_cmsit-ups countsbroad jump_cmgenderclass
age1.000-0.234-0.0510.2110.1790.205-0.122-0.092-0.498-0.3650.1460.072
height_cm-0.2341.0000.759-0.5160.1530.2260.744-0.2540.4850.6840.7590.063
weight_kg-0.0510.7591.000-0.1560.2670.3500.728-0.3180.3310.5350.7200.158
body fat_%0.211-0.516-0.1561.0000.046-0.041-0.550-0.032-0.590-0.6760.5490.223
diastolic0.1790.1530.2670.0461.0000.6750.208-0.0930.0070.0990.2160.046
systolic0.2050.2260.350-0.0410.6751.0000.293-0.1070.0620.1690.3120.031
gripForce-0.1220.7440.728-0.5500.2080.2931.000-0.1550.5750.7630.8670.140
sit and bend forward_cm-0.092-0.254-0.318-0.032-0.093-0.107-0.1551.0000.157-0.0200.2930.276
sit-ups counts-0.4980.4850.331-0.5900.0070.0620.5750.1571.0000.7340.4730.285
broad jump_cm-0.3650.6840.535-0.6760.0990.1690.763-0.0200.7341.0000.7300.180
gender0.1460.7590.7200.5490.2160.3120.8670.2930.4730.7301.0000.091
class0.0720.0630.1580.2230.0460.0310.1400.2760.2850.1800.0911.000

Missing values

2023-06-24T14:14:44.745119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-24T14:14:44.998674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agegenderheight_cmweight_kgbody fat_%diastolicsystolicgripForcesit and bend forward_cmsit-ups countsbroad jump_cmclass
027.0M172.375.2421.380.0130.054.918.460.0217.0C
125.0M165.055.8015.777.0126.036.416.353.0229.0A
231.0M179.678.0020.192.0152.044.812.049.0181.0C
332.0M174.571.1018.476.0147.041.415.253.0219.0B
428.0M173.867.7017.170.0127.043.527.145.0217.0B
536.0F165.455.4022.064.0119.023.821.027.0153.0B
642.0F164.563.7032.272.0135.022.70.818.0146.0D
733.0M174.977.2036.984.0137.045.912.342.0234.0B
854.0M166.867.5027.685.0165.040.418.634.0148.0C
928.0M185.084.6014.481.0156.057.912.155.0213.0B
agegenderheight_cmweight_kgbody fat_%diastolicsystolicgripForcesit and bend forward_cmsit-ups countsbroad jump_cmclass
1338325.0M170.768.8613.360.0106.039.214.151.0235.0B
1338464.0F152.455.9033.187.0158.023.520.014.0154.0B
1338537.0M177.583.1029.777.0113.041.77.241.0167.0D
1338662.0F156.240.0020.261.0115.018.55.71.081.0D
1338739.0M174.470.8024.378.0132.041.612.044.0168.0B
1338825.0M172.171.8016.274.0141.035.817.447.0198.0C
1338921.0M179.763.9012.174.0128.033.01.148.0167.0D
1339039.0M177.280.5020.178.0132.063.516.445.0229.0A
1339164.0F146.157.7040.468.0121.019.39.20.075.0D
1339234.0M164.066.1019.582.0150.035.97.151.0180.0C

Duplicate rows

Most frequently occurring

agegenderheight_cmweight_kgbody fat_%diastolicsystolicgripForcesit and bend forward_cmsit-ups countsbroad jump_cmclass# duplicates
027.0F157.049.130.770.086.027.719.751.0167.0A2